1. Field of the Invention
The invention relates to image segmentation methods, and more particularly to an image segmentation method and system based on region features of pixels.
2. Description of the Related Art
“Image segmentation” segments target objects from an input image, which is applied to image recognition, image compression, image search, and monitor systems. Conventional image segmentation methods comprise a histogram-based image segmentation method, an edge-based image segmentation method, and a region-based image segmentation method.
The histogram-based image segmentation method analyzes the histogram of the whole or a portion of an input image to determine a proper threshold for image segmentation. The edge-based image segmentation method analyzes brightness variations between an image object and the background of the image object to find the edge of the image object for image segmentation. The region-based image segmentation method determines brightness similarities between local object images for image segmentation.
The histogram-based image segmentation method is simple and easy to implement but determination of a proper threshold is a big challenge. Additionally, analyzing the histogram without referring to region features of an image may provide tolerance for image noise but does not generate acceptable segmentation results for complicated images.
The edge-based image segmentation method focuses on analyzing brightness variations of an image that is sensitive to noise reactions. Additionally it is difficult to accomplish image segmentation based on unobvious edges if brightness of an image object is slowly and progressively increasing or decreasing.
The region-based image segmentation method must first designate seeds and repeatedly scans pixels of an image. Next, region growing is performed from the seeds by collecting similar neighboring pixels, thus completing image segmentation and the region-based image segmentation method is sensitive to noise reactions and causes over segmentation.
Connected component labeling assigns different marks to each image object to efficiently use and analyze image segmentation results. For the histogram-based and edge-based image segmentation methods, when the image segmentation is complete, connected components labeling is additionally performed to mark each of the segmented objects.
When an image is segmented using the region-based image segmentation method, individual marks can be simultaneously assigned to each of the segmented regions. However, such kind of image segmentation methods must perform multiple image processing methods to reduce interference generated by noise. Additionally, selection of seeds and repeated time-consuming operations are inefficient.
Current image segmentation technology is pixel-based and classifies neighboring pixels having similar features to regions providing identical signs, thereby to complete image segmentation. However, the major deficiency of current image segmentation methods with pixel-based comparison is that the segmentation result is highly sensitive to noise. In other words, current image segmentation methods must remove noise using image processing methods comprising, for example, smoothing, edge enhancement, color quantization, and the like.
Thus, an image segmentation method and system based on region features of pixels is desirable, synchronously performing image segmentation and object marking and providing real-time requirements for high performance.